library(ggplot2)
library(reshape2)
library(doBy)
loadData <- function(FILE,desc,label1,label2)
{
d <- read.csv(FILE,header = FALSE)
if(label2 > label1)
{
  tmp <- label2
  label2 <- label1
  label1 <- tmp
  tmp <- d[1]
  d[1] <- d[2]
  d[2] <- tmp
}
d$TimeStamp <- 1:nrow(d)
names(d) <- c(paste("Tenant 1 @",label1,"MB/s"), paste("Tenant 2 @",label2,"MB/s"),"Timestamp")
d <- melt(d,id.vars=c("Timestamp"))
names(d) <- c("Timestamp","Tenant","BW")
d$desc <- desc
d$label1 <- label1
d$label2 <- label2
return (d)
}
loadData1 <- function(FILE,desc,label1)
{
d <- read.csv(FILE,header = FALSE)
d$TimeStamp <- 1:nrow(d)
names(d) <- c("Tenant 1", "Timestamp")
d <- melt(d,id.vars=c("Timestamp"))
names(d) <- c("Timestamp","Tenant","BW")
d$desc <- desc
return (d)
}
draw1 <- function(dades)
{
  ggplot(dades)+geom_line(aes(x=Timestamp,y=BW,colour=Tenant,linetype=Tenant))+theme_bw(base_size = 14)+labs(title=(dades$desc))+theme( legend.position = "top")
}
drawsmooth <- function(dades)
{
  ggplot(dades)+geom_smooth(aes(x=Timestamp,y=BW,colour=Tenant,linetype=Tenant), degree=0, 
        span=0.05, se=FALSE)+theme_bw(base_size = 14)+labs(title=(dades$desc))+theme( legend.position = "top")
}
draw2 <- function(dades)
{
  ggplot(dades)+geom_boxplot(aes(x=Tenant,y=BW))+theme_bw()+labs(title=(dades$desc))
}
draw3 <- function(dades)
{
  ggplot(dades)+geom_violin(aes(x=Tenant,y=BW),draw_quantiles = c(0.5))+theme_bw()+labs(title=(dades$desc))
}
draw4 <- function(dades)
{
  ggplot(dades,aes(colour=Tenant,x=BW,linetype=Tenant))+ stat_ecdf(geom = "step")+geom_vline(xintercept=dades$label1,colour="black")+geom_vline(xintercept=dades$label2,colour="black")+theme_bw(base_size = 14)+labs(title=(dades$desc))+scale_y_continuous("ecdf")+theme(legend.position = "top")
}
 

Selected LOG1.CSV URV 100 / 20

Shows how the mechanism guarantee the 20 MB/s but does not guarantee the 100 MB/s ## Line plot + Violin plot

URV10020 <- loadData("../log1/log1.csv","URV 100 / 20", 100, 20)
draw1(URV10020)

drawsmooth(URV10020)+scale_y_continuous(limits=c(0,250))
Ignoring unknown parameters: degree
pdf(file="URV100_20_Timeline.pdf",width = 7, height= 5)
drawsmooth(URV10020)+scale_y_continuous(limits=c(0,250))
Ignoring unknown parameters: degree
dev.off()
png 
  2 

draw2(URV10020)

draw3(URV10020)

draw4(URV10020)+scale_x_continuous(limits=c(0,250))
pdf(file="URV100_20_ECDF.pdf",width = 7, height= 5)
draw4(URV10020)+scale_x_continuous(limits=c(0,250))
dev.off()
png 
  2 

summaryBy(.~Tenant,URV10020,id = ~Timestamp,FUN = c(quantile))

BSC filter allows to request a 100 / 20 BW and the result is fulfilled on the median of the values gathered each second.

dades <- loadData("../log14/log14.csv","BSC 100 / 20",100,20)
draw1(dades)

drawsmooth(dades)+scale_y_continuous(limits=c(0,250))
Ignoring unknown parameters: degree
pdf(file="BSC100_20_Timeline.pdf",width = 7, height= 5)
drawsmooth(dades)+scale_y_continuous(limits=c(0,250))
Ignoring unknown parameters: degree
dev.off()
png 
  2 

draw2(dades)

draw3(dades)

draw4(dades)
pdf(file="BSC100_20_ECDF.pdf",width = 7, height= 5)
draw4(dades)+scale_x_continuous(limits=c(0,250))
dev.off()
png 
  2 

Selected LOG33 URV 50 / 20

Shows that asking less BW (100 -> 50) allows to obtain the requested value for a tenant but does not allow to obtain it for the other one (asked 20 and obtained 10)

dades <- loadData("../log33/log33.csv","URV 50 / 20",50,20)
draw1(dades)

drawsmooth(dades)+scale_y_continuous(limits=c(0,100))
Ignoring unknown parameters: degree
pdf(file="URV50_20_Timeline.pdf",width = 7, height= 5)
drawsmooth(dades)+scale_y_continuous(limits=c(0,100))
Ignoring unknown parameters: degree
dev.off()
png 
  2 

draw2(dades)

draw3(dades)

draw4(dades)+scale_x_continuous(limits=c(0,120))
pdf(file="URV50_20_ECDF.pdf",width = 7, height= 5)
draw4(dades)+scale_x_continuous(limits=c(0,120))
dev.off()
png 
  2 

summaryBy(.~Tenant,URV10020,id = ~Timestamp,FUN = c(quantile))
dades <- loadData("../log27/log27.csv","BSC 50 / 20",20,50)
draw1(dades)

drawsmooth(dades)+scale_y_continuous(limits=c(0,100))
Ignoring unknown parameters: degree
pdf(file="BSC50_20_Timeline.pdf",width = 7, height= 5)
drawsmooth(dades)+scale_y_continuous(limits=c(0,100))
Ignoring unknown parameters: degree
dev.off()
png 
  2 

draw2(dades)

draw3(dades)

draw4(dades)+scale_x_continuous(limits=c(0,120))
pdf(file="BSC50_20_ECDF.pdf",width = 7, height= 5)
draw4(dades)+scale_x_continuous(limits=c(0,120))
dev.off()
png 
  2 

Experiment to show how exceeding the capabilities of the system works URV 100 / 100. Requests should go to different object stores so the results should be higher. But the median falls below the 40 MB/s

dades <- loadData("../log6/log6.csv","URV 100 / 100",100,100)
draw1(dades)

drawsmooth(dades)+scale_y_continuous(limits=c(0,100))
Ignoring unknown parameters: degree
pdf(file="URV100_100_Timeline.pdf",width = 7, height= 5)
drawsmooth(dades)+scale_y_continuous(limits=c(0,100))
Ignoring unknown parameters: degree
dev.off()
png 
  2 

draw2(dades)

draw3(dades)

draw4(dades)+scale_x_continuous(limits=c(0,120))
pdf(file="URV100_100_ECDF.pdf",width = 7, height= 5)
draw4(dades)+scale_x_continuous(limits=c(0,120))
dev.off()
png 
  2 

dades <- loadData("../log35/log35.csv","BSC 100 / 100",100,100)
draw1(dades)

drawsmooth(dades)+scale_y_continuous(limits=c(0,100))
Ignoring unknown parameters: degree
pdf(file="BSC100_100_Timeline.pdf",width = 7, height= 5)
drawsmooth(dades)+scale_y_continuous(limits=c(0,100))
Ignoring unknown parameters: degree
dev.off()
png 
  2 

draw2(dades)

draw3(dades)

draw4(dades)+scale_x_continuous(limits=c(0,120))
pdf(file="BSC100_100_ECDF.pdf",width = 7, height= 5)
draw4(dades)+scale_x_continuous(limits=c(0,120))
dev.off()
png 
  2 

Experiment to show how exceeding the capabilities of the system works URV 50 / 50 Requests should go to different object stores so the results should be higher. But the median falls below the 40 MB/s. The results are worse than the 100/100 scenario.

dades <- loadData("../log8/log8.csv","URV 50 / 50",50,50)
draw1(dades)

drawsmooth(dades)+scale_y_continuous(limits=c(0,250))
Ignoring unknown parameters: degree
pdf(file="URV50_50_Timeline.pdf",width = 7, height= 5)
drawsmooth(dades)+scale_y_continuous(limits=c(0,250))
Ignoring unknown parameters: degree
dev.off()
png 
  2 

draw2(dades)

draw3(dades)

draw4(dades)+scale_x_continuous(limits=c(0,250))
pdf(file="URV50_50_ECDF.pdf",width = 7, height= 5)
draw4(dades)+scale_x_continuous(limits=c(0,250))
dev.off()
png 
  2 

A less demanding bw differentation, for example 50 / 50 shows how we exceed the asked capabilities.

dades <- loadData("../log11/log11.csv","BSC 50 / 50",50,50)
draw1(dades)

drawsmooth(dades)+scale_y_continuous(limits=c(0,250))
Ignoring unknown parameters: degree
pdf(file="BSC50_50_Timeline.pdf",width = 7, height= 5)
drawsmooth(dades)+scale_y_continuous(limits=c(0,250))
Ignoring unknown parameters: degree
dev.off()
png 
  2 

draw2(dades)

draw3(dades)

draw4(dades)+scale_x_continuous(limits=c(0,250))
pdf(file="BSC50_50_ECDF.pdf",width = 7, height= 5)
draw4(dades)+scale_x_continuous(limits=c(0,250))
dev.off()
png 
  2 

dades <- loadData("../log38/log38.csv","URV 20 / 10",20,10)
draw1(dades)

drawsmooth(dades)+scale_y_continuous(limits=c(0,250))
Ignoring unknown parameters: degree
pdf(file="URV20_10_Timeline.pdf",width = 7, height= 5)
drawsmooth(dades)+scale_y_continuous(limits=c(0,250))
Ignoring unknown parameters: degree
dev.off()
png 
  2 

draw2(dades)

draw3(dades)

draw4(dades)+scale_x_continuous(limits=c(0,250))
pdf(file="URV20_10_ECDF.pdf",width = 7, height= 5)
draw4(dades)+scale_x_continuous(limits=c(0,250))
dev.off()
png 
  2 

dades <- loadData("../log40/log40.csv","BSC 20 / 10",20,10)
draw1(dades)

drawsmooth(dades)+scale_y_continuous(limits=c(0,250))
Ignoring unknown parameters: degree
pdf(file="BSC20_10_Timeline.pdf",width = 7, height= 5)
drawsmooth(dades)+scale_y_continuous(limits=c(0,250))
Ignoring unknown parameters: degree
dev.off()
png 
  2 

draw2(dades)

draw3(dades)

draw4(dades)+scale_x_continuous(limits=c(0,250))
pdf(file="BSC20_10_ECDF.pdf",width = 7, height= 5)
draw4(dades)+scale_x_continuous(limits=c(0,250))
dev.off()
png 
  2 

dades <- loadData("../log19/log19.csv","URV 50 / 20 / Outside interference of 10 MB/s ",50,20)
draw1(dades)

drawsmooth(dades)+scale_y_continuous(limits=c(0,200))
Ignoring unknown parameters: degree
pdf(file="URV50_20_10i_Timeline.pdf",width = 7, height= 5)
drawsmooth(dades)+scale_y_continuous(limits=c(0,200))
Ignoring unknown parameters: degree
dev.off()
png 
  2 

draw2(dades)

draw3(dades)

draw4(dades)+scale_x_continuous(limits=c(0,200))
pdf(file="URV50_20_10i_ECDF.pdf",width = 7, height= 5)
draw4(dades)+scale_x_continuous(limits=c(0,200))
dev.off()
png 
  2 

dades <- loadData("../log26/log26.csv","BSC 50 / 20 / Outside interference of 10 MB/s",20,50)
draw1(dades)

drawsmooth(dades)+scale_y_continuous(limits=c(0,200))
Ignoring unknown parameters: degree
pdf(file="BSC50_20_10i_Timeline.pdf",width = 7, height= 5)
drawsmooth(dades)+scale_y_continuous(limits=c(0,200))
Ignoring unknown parameters: degree
dev.off()
png 
  2 

draw2(dades)

draw3(dades)

draw4(dades)+scale_x_continuous(limits=c(0,200))
pdf(file="BSC50_20_10i_ECDF.pdf",width = 7, height= 5)
draw4(dades)+scale_x_continuous(limits=c(0,200))
dev.off()
png 
  2 

dades <- loadData("../log20/log20.csv","URV 50 / 50 / Outside interference of 10 MB/s ",50,50)
draw1(dades)

drawsmooth(dades)
Ignoring unknown parameters: degree

draw2(dades)

draw3(dades)

draw4(dades)

dades <- loadData("../log36/log36.csv","BSC 50 / 20 / Outside interference of 20 MB/s",50,20)
draw1(dades)

drawsmooth(dades)+scale_y_continuous(limits=c(0,150))
Ignoring unknown parameters: degree
pdf(file="BSC50_20_20i_Timeline.pdf",width = 7, height= 5)
drawsmooth(dades)+scale_y_continuous(limits=c(0,150))
Ignoring unknown parameters: degree
dev.off()
png 
  2 

draw2(dades)

draw3(dades)

draw4(dades)+scale_x_continuous(limits=c(0,150))
pdf(file="BSC50_20_20i_ECDF.pdf",width = 7, height= 5)
draw4(dades)+scale_x_continuous(limits=c(0,150))
dev.off()
png 
  2 

dades <- loadData("../log32/log32.csv","BSC 100 / 20 / Outside interference of 20 MB/s",20,100)
draw1(dades)

dades <- loadData("../log29/log29.csv","BSC 100 / 20 / Outside interference of 10 MB/s",20,100)
draw1(dades)

drawsmooth(dades)
Ignoring unknown parameters: degree

draw2(dades)

draw3(dades)

draw4(dades)

dades <- loadData("../log30/log30.csv","BSC 100 / 20 / Outside interference of 10 MB/s",20,100)
draw1(dades)

drawsmooth(dades)
Ignoring unknown parameters: degree

draw2(dades)

draw3(dades)

draw4(dades)

dades <- loadData("../log31/log31.csv","BSC 100 / 20 / Outside interference of 10 MB/s",20,100)
draw1(dades)

drawsmooth(dades)
Ignoring unknown parameters: degree

draw2(dades)

draw3(dades)

draw4(dades)

---
title: "R Notebook"
output:
  pdf_document: default
  html_notebook: default
---

```{r}
library(ggplot2)
library(reshape2)
library(doBy)
loadData <- function(FILE,desc,label1,label2)
{
d <- read.csv(FILE,header = FALSE)
if(label2 > label1)
{
  tmp <- label2
  label2 <- label1
  label1 <- tmp
  tmp <- d[1]
  d[1] <- d[2]
  d[2] <- tmp
}
d$TimeStamp <- 1:nrow(d)
names(d) <- c(paste("Tenant 1 @",label1,"MB/s"), paste("Tenant 2 @",label2,"MB/s"),"Timestamp")
d <- melt(d,id.vars=c("Timestamp"))
names(d) <- c("Timestamp","Tenant","BW")
d$desc <- desc
d$label1 <- label1
d$label2 <- label2
return (d)
}

loadData1 <- function(FILE,desc,label1)
{
d <- read.csv(FILE,header = FALSE)
d$TimeStamp <- 1:nrow(d)
names(d) <- c("Tenant 1", "Timestamp")
d <- melt(d,id.vars=c("Timestamp"))
names(d) <- c("Timestamp","Tenant","BW")
d$desc <- desc
return (d)
}

draw1 <- function(dades)
{
  ggplot(dades)+geom_line(aes(x=Timestamp,y=BW,colour=Tenant,linetype=Tenant))+theme_bw(base_size = 14)+labs(title=(dades$desc))+theme( legend.position = "top")
}
drawsmooth <- function(dades)
{
  ggplot(dades)+geom_smooth(aes(x=Timestamp,y=BW,colour=Tenant,linetype=Tenant), degree=0, 
        span=0.05, se=FALSE)+theme_bw(base_size = 14)+labs(title=(dades$desc))+theme( legend.position = "top")
}
draw2 <- function(dades)
{
  ggplot(dades)+geom_boxplot(aes(x=Tenant,y=BW))+theme_bw()+labs(title=(dades$desc))
}
draw3 <- function(dades)
{
  ggplot(dades)+geom_violin(aes(x=Tenant,y=BW),draw_quantiles = c(0.5))+theme_bw()+labs(title=(dades$desc))
}

draw4 <- function(dades)
{
  ggplot(dades,aes(colour=Tenant,x=BW,linetype=Tenant))+ stat_ecdf(geom = "step")+geom_vline(xintercept=dades$label1,colour="black")+geom_vline(xintercept=dades$label2,colour="black")+theme_bw(base_size = 14)+labs(title=(dades$desc))+scale_y_continuous("ecdf")+theme(legend.position = "top")
}
 

```

# Selected LOG1.CSV URV 100 / 20
Shows how the mechanism guarantee the 20 MB/s but does not guarantee the 100 MB/s
## Line plot + Violin plot


```{r}
URV10020 <- loadData("../log1/log1.csv","URV 100 / 20", 100, 20)
draw1(URV10020)

drawsmooth(URV10020)+scale_y_continuous(limits=c(0,250))
pdf(file="URV100_20_Timeline.pdf",width = 7, height= 5)
drawsmooth(URV10020)+scale_y_continuous(limits=c(0,250))
dev.off()
draw2(URV10020)
draw3(URV10020)
draw4(URV10020)+scale_x_continuous(limits=c(0,250))
pdf(file="URV100_20_ECDF.pdf",width = 7, height= 5)
draw4(URV10020)+scale_x_continuous(limits=c(0,250))
dev.off()
summaryBy(.~Tenant,URV10020,id = ~Timestamp,FUN = c(quantile))
```
# BSC filter allows to request a 100 / 20 BW and the result is fulfilled on the median of the values gathered each second.

```{r}
dades <- loadData("../log14/log14.csv","BSC 100 / 20",100,20)
draw1(dades)
drawsmooth(dades)+scale_y_continuous(limits=c(0,250))
pdf(file="BSC100_20_Timeline.pdf",width = 7, height= 5)
drawsmooth(dades)+scale_y_continuous(limits=c(0,250))
dev.off()
draw2(dades)
draw3(dades)
draw4(dades)
pdf(file="BSC100_20_ECDF.pdf",width = 7, height= 5)
draw4(dades)+scale_x_continuous(limits=c(0,250))
dev.off()
```


# Selected LOG33 URV 50 / 20 
Shows that asking less BW (100 -> 50) allows to obtain the requested value for a tenant but does not allow to obtain it for the other one (asked 20 and obtained 10)

```{r}
dades <- loadData("../log33/log33.csv","URV 50 / 20",50,20)
draw1(dades)
drawsmooth(dades)+scale_y_continuous(limits=c(0,100))
pdf(file="URV50_20_Timeline.pdf",width = 7, height= 5)
drawsmooth(dades)+scale_y_continuous(limits=c(0,100))
dev.off()
draw2(dades)
draw3(dades)
draw4(dades)+scale_x_continuous(limits=c(0,120))
pdf(file="URV50_20_ECDF.pdf",width = 7, height= 5)
draw4(dades)+scale_x_continuous(limits=c(0,120))
dev.off()
summaryBy(.~Tenant,URV10020,id = ~Timestamp,FUN = c(quantile))
```
```{r}
dades <- loadData("../log27/log27.csv","BSC 50 / 20",20,50)
draw1(dades)
drawsmooth(dades)+scale_y_continuous(limits=c(0,100))
pdf(file="BSC50_20_Timeline.pdf",width = 7, height= 5)
drawsmooth(dades)+scale_y_continuous(limits=c(0,100))
dev.off()
draw2(dades)
draw3(dades)
draw4(dades)+scale_x_continuous(limits=c(0,120))
pdf(file="BSC50_20_ECDF.pdf",width = 7, height= 5)
draw4(dades)+scale_x_continuous(limits=c(0,120))
dev.off()
```



# Experiment to show how exceeding the capabilities of the system works URV 100 / 100. Requests should go to different object stores so the results should be higher. But the median falls below the 40 MB/s
```{r}
dades <- loadData("../log6/log6.csv","URV 100 / 100",100,100)
draw1(dades)
drawsmooth(dades)+scale_y_continuous(limits=c(0,100))
pdf(file="URV100_100_Timeline.pdf",width = 7, height= 5)
drawsmooth(dades)+scale_y_continuous(limits=c(0,100))
dev.off()
draw2(dades)
draw3(dades)
draw4(dades)+scale_x_continuous(limits=c(0,120))
pdf(file="URV100_100_ECDF.pdf",width = 7, height= 5)
draw4(dades)+scale_x_continuous(limits=c(0,120))
dev.off()
```
```{r}
dades <- loadData("../log35/log35.csv","BSC 100 / 100",100,100)
draw1(dades)
drawsmooth(dades)+scale_y_continuous(limits=c(0,100))
pdf(file="BSC100_100_Timeline.pdf",width = 7, height= 5)
drawsmooth(dades)+scale_y_continuous(limits=c(0,100))
dev.off()
draw2(dades)
draw3(dades)
draw4(dades)+scale_x_continuous(limits=c(0,120))
pdf(file="BSC100_100_ECDF.pdf",width = 7, height= 5)
draw4(dades)+scale_x_continuous(limits=c(0,120))
dev.off()
```
# Experiment to show how exceeding the capabilities of the system works URV 50 / 50 Requests should go to different object stores so the results should be higher. But the median falls below the 40 MB/s. The results are worse than the 100/100 scenario.

```{r}
dades <- loadData("../log8/log8.csv","URV 50 / 50",50,50)
draw1(dades)
drawsmooth(dades)+scale_y_continuous(limits=c(0,250))
pdf(file="URV50_50_Timeline.pdf",width = 7, height= 5)
drawsmooth(dades)+scale_y_continuous(limits=c(0,250))
dev.off()
draw2(dades)
draw3(dades)
draw4(dades)+scale_x_continuous(limits=c(0,250))
pdf(file="URV50_50_ECDF.pdf",width = 7, height= 5)
draw4(dades)+scale_x_continuous(limits=c(0,250))
dev.off()
```


# A less demanding bw differentation, for example 50 / 50 shows how we exceed the asked capabilities. 
```{r}
dades <- loadData("../log11/log11.csv","BSC 50 / 50",50,50)
draw1(dades)
drawsmooth(dades)+scale_y_continuous(limits=c(0,250))
pdf(file="BSC50_50_Timeline.pdf",width = 7, height= 5)
drawsmooth(dades)+scale_y_continuous(limits=c(0,250))
dev.off()
draw2(dades)
draw3(dades)
draw4(dades)+scale_x_continuous(limits=c(0,250))
pdf(file="BSC50_50_ECDF.pdf",width = 7, height= 5)
draw4(dades)+scale_x_continuous(limits=c(0,250))
dev.off()
```

```{r}
dades <- loadData("../log38/log38.csv","URV 20 / 10",20,10)
draw1(dades)
drawsmooth(dades)+scale_y_continuous(limits=c(0,250))
pdf(file="URV20_10_Timeline.pdf",width = 7, height= 5)
drawsmooth(dades)+scale_y_continuous(limits=c(0,250))
dev.off()
draw2(dades)
draw3(dades)
draw4(dades)+scale_x_continuous(limits=c(0,250))
pdf(file="URV20_10_ECDF.pdf",width = 7, height= 5)
draw4(dades)+scale_x_continuous(limits=c(0,250))
dev.off()
```

```{r}
dades <- loadData("../log40/log40.csv","BSC 20 / 10",20,10)
draw1(dades)
drawsmooth(dades)+scale_y_continuous(limits=c(0,250))
pdf(file="BSC20_10_Timeline.pdf",width = 7, height= 5)
drawsmooth(dades)+scale_y_continuous(limits=c(0,250))
dev.off()
draw2(dades)
draw3(dades)
draw4(dades)+scale_x_continuous(limits=c(0,250))
pdf(file="BSC20_10_ECDF.pdf",width = 7, height= 5)
draw4(dades)+scale_x_continuous(limits=c(0,250))
dev.off()
```


```{r}
dades <- loadData("../log19/log19.csv","URV 50 / 20 / Outside interference of 10 MB/s ",50,20)
draw1(dades)
drawsmooth(dades)+scale_y_continuous(limits=c(0,200))
pdf(file="URV50_20_10i_Timeline.pdf",width = 7, height= 5)
drawsmooth(dades)+scale_y_continuous(limits=c(0,200))
dev.off()
draw2(dades)
draw3(dades)
draw4(dades)+scale_x_continuous(limits=c(0,200))
pdf(file="URV50_20_10i_ECDF.pdf",width = 7, height= 5)
draw4(dades)+scale_x_continuous(limits=c(0,200))
dev.off()
```
```{r}
dades <- loadData("../log26/log26.csv","BSC 50 / 20 / Outside interference of 10 MB/s",20,50)
draw1(dades)
drawsmooth(dades)+scale_y_continuous(limits=c(0,200))
pdf(file="BSC50_20_10i_Timeline.pdf",width = 7, height= 5)
drawsmooth(dades)+scale_y_continuous(limits=c(0,200))
dev.off()
draw2(dades)
draw3(dades)
draw4(dades)+scale_x_continuous(limits=c(0,200))
pdf(file="BSC50_20_10i_ECDF.pdf",width = 7, height= 5)
draw4(dades)+scale_x_continuous(limits=c(0,200))
dev.off()
```


```{r}
dades <- loadData("../log20/log20.csv","URV 50 / 50 / Outside interference of 10 MB/s ",50,50)
draw1(dades)
drawsmooth(dades)
draw2(dades)
draw3(dades)
draw4(dades)
```

```{r}
dades <- loadData("../log21/log21.csv","URV 50 / 20 / Outside interference of 20 MB/s ",50,20)
draw1(dades)
drawsmooth(dades)+scale_y_continuous(limits=c(0,150))
pdf(file="URV50_20_20i_Timeline.pdf",width = 7, height= 5)
drawsmooth(dades)+scale_y_continuous(limits=c(0,150))
dev.off()
draw2(dades)
draw3(dades)
draw4(dades)+scale_x_continuous(limits=c(0,150))
pdf(file="URV50_20_20i_ECDF.pdf",width = 7, height= 5)
draw4(dades)+scale_x_continuous(limits=c(0,150))
dev.off()
```

```{r}
dades <- loadData("../log36/log36.csv","BSC 50 / 20 / Outside interference of 20 MB/s",50,20)
draw1(dades)
drawsmooth(dades)+scale_y_continuous(limits=c(0,150))
pdf(file="BSC50_20_20i_Timeline.pdf",width = 7, height= 5)
drawsmooth(dades)+scale_y_continuous(limits=c(0,150))
dev.off()
draw2(dades)
draw3(dades)
draw4(dades)+scale_x_continuous(limits=c(0,150))
pdf(file="BSC50_20_20i_ECDF.pdf",width = 7, height= 5)
draw4(dades)+scale_x_continuous(limits=c(0,150))
dev.off()
```


```{r}
dades <- loadData("../log32/log32.csv","BSC 100 / 20 / Outside interference of 20 MB/s",20,100)
draw1(dades)
drawsmooth(dades)
draw2(dades)
draw3(dades)
draw4(dades)
```



```{r}
dades <- loadData("../log29/log29.csv","BSC 100 / 20 / Outside interference of 10 MB/s",20,100)
draw1(dades)
drawsmooth(dades)
draw2(dades)
draw3(dades)
draw4(dades)
```

```{r}
dades <- loadData("../log30/log30.csv","BSC 100 / 20 / Outside interference of 10 MB/s",20,100)
draw1(dades)
drawsmooth(dades)
draw2(dades)
draw3(dades)
draw4(dades)
```


```{r}
dades <- loadData("../log31/log31.csv","BSC 100 / 20 / Outside interference of 10 MB/s",20,100)
draw1(dades)
drawsmooth(dades)
draw2(dades)
draw3(dades)
draw4(dades)
```





